code: def check_compatibility(
      self, model_keras, model_objax, delta=1.001e-06
  ):
      data = np.random.uniform(size=(2, 3, 224, 224))
      # training=False
      output_keras = model_keras(
          data.transpose((0, 2, 3, 1)), training=False
      ).numpy()
      if output_keras.ndim == 4:
          output_keras = output_keras.transpose((0, 3, 1, 2))
      output_objax = model_objax(data, training=False)
      sq_diff = (output_objax - output_keras) ** 2
      self.assertAlmostEqual(sq_diff.mean(), 0, delta=delta)
      # training=True
      output_keras = model_keras(
          data.transpose((0, 2, 3, 1)), training=True
      ).numpy()
      if output_keras.ndim == 4:
          output_keras = output_keras.transpose((0, 3, 1, 2))
      output_objax = model_objax(data, training=True)
      sq_diff = (output_objax - output_keras) ** 2
      self.assertAlmostEqual(sq_diff.mean(), 0, delta=delta)
  
file_path: objax/tests/resnet_v2.py
imports: import unittest
  from parameterized import parameterized
  from functools import partial
  import numpy
  import tensorflow
  import objax
  from objax.zoo.resnet_v2 import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, ResNet200
  from objax.zoo.resnet_v2 import convert_keras_model, load_pretrained_weights_from_keras
label_desc: Use image node_id 1 for calling the TestResNetV2Pretrained obj's underlying member method code with example usage: obj.check_compatibility(model_keras, model_objax, delta) without return types